Physical signs of patients indicate crucial evidence for diagnosing both location and nature of the disease, where there is a sequential relationship between the two tasks. Thus their joint learning can utilize intrinsic association by transferring related knowledge across relevant tasks. Choosing the right time to transfer is a critical problem for joint learning. However, how to dynamically adjust when tasks interact to capture the right time for transferring related knowledge is still an open issue. To this end, we propose a Task-Coupling Elastic Learning (TCEL) framework to model the task relatedness for classifying disease-location and disease-nature based on physical sign images. The main idea is to dynamically transfer relevant knowledge by progressively shifting task-coupling from loose to tight during the multi-stage training. In the early stage of training, we relax the constraints of modeling relations to focus more in learning the generic task-common features. In the later stage, the semantic guidance will be strengthened to learn the task-specific features. Specifically, a dynamic sequential module (DSM) is proposed to explicitly model the sequential relationship and enable multi-stage training. Moreover, to address the side effect of DSM, a new loss regularization is proposed. The extensive experiments on these two clinical datasets show the superiority of the proposed method over the baselines, and demonstrate the effectiveness of the proposed task-coupling elastic mechanism.

中文翻译：

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基于物理标志的医学图像分类的任务耦合弹性学习

患者的体征表明诊断疾病的位置和性质的关键证据，其中两项任务之间存在顺序关系。因此，他们的联合学习可以通过在相关任务之间转移相关知识来利用内在关联。选择合适的迁移时间是联合学习的关键问题。然而，如何在任务交互时动态调整以捕捉正确的时间来传递相关知识仍然是一个悬而未决的问题。为此，我们提出了一个任务耦合弹性学习（TCEL）框架来建模任务相关性，以基于物理标志图像对疾病位置和疾病性质进行分类。主要思想是在多阶段训练期间通过逐步将任务耦合从松散转变为紧密来动态转移相关知识。在训练的早期阶段，我们放松了建模关系的约束，更加专注于学习通用的任务通用特征。后期将加强语义引导，学习特定任务的特征。具体来说，提出了一个动态序列模块（DSM）来显式地建模序列关系并实现多阶段训练。此外，为了解决 DSM 的副作用，提出了一种新的损失正则化。在这两个临床数据集上的广泛实验表明了所提出的方法优于基线，并证明了所提出的任务耦合弹性机制的有效性。将加强语义指导以学习特定任务的特征。具体来说，提出了一个动态序列模块（DSM）来显式地建模序列关系并实现多阶段训练。此外，为了解决 DSM 的副作用，提出了一种新的损失正则化。在这两个临床数据集上的广泛实验表明了所提出的方法优于基线，并证明了所提出的任务耦合弹性机制的有效性。将加强语义指导以学习特定任务的特征。具体来说，提出了一个动态序列模块（DSM）来显式地建模序列关系并实现多阶段训练。此外，为了解决 DSM 的副作用，提出了一种新的损失正则化。在这两个临床数据集上的广泛实验表明了所提出的方法优于基线，并证明了所提出的任务耦合弹性机制的有效性。